{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本章节目标\n",
    "\n",
    "* 使用plt.subplot: 绘制多表格图\n",
    "* 泰坦尼克号 多子图的应用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多子图展示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "并排比较数据的不同视图。这些子图可能是插页、绘图网格或其他更复杂的布局，方便数据的比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "# plt.style.use('seaborn-white')\n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 题1 - 使用``fig.add_subplot``: 绘制多表格图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z39vAQ5l5H3At8GhPe6uWmWcrPpYe+izUg77/hBv3Eu8hqdnf6cw81zyeB9bqy/Shz0I96Hvo417iPSQ1+5uNiLsj4mpgBjjVz9Y6N/RZqAe9XkwVETcAx4D/pLnEG/jHxVdwtqzZXvGyuDeVz+HHwCEggJcz85lJ7LVGRMxl5nRE3AE8uZZmoX70fsVl8676w8DrmfnZuGsmaej769J6eq5q52XZkop8I0pSkSEhqciQkFRkSEgqMiQkFf0/6uagWFXc67QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 多子图添加文字标注"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "for i in range(1, 7):\n",
    "    fig.add_subplot(2, 3, i)\n",
    "    plt.text(0.5, 0.5, str((2, 3, i)),fontsize=18, ha='center',va='center')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个函数 ``plt.subplots_adjust`` 可以用于调整不同子图之间的距离,设置横向和纵向距离:\n",
    "- hspace\n",
    "- wspace\n",
    "\n",
    "#### 题2 ： 使用面向对象的函数 ``fig.add_subplot()`` 绘制图表2行3列，同时在每个子图中添加文字自定义标注"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述图标中含有大量的内部标签，下面我们需要优化，去除没有用的内部标签内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2, 3, sharex='col', sharey='row')\n",
    "fig.patch.set_facecolor('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过指定 sharex 和 sharey ，图标中子图自动移除内部的标签.下面我们为创建的图标添加数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axes are in a two-dimensional array, indexed by [row, col]\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        ax[i, j].text(0.5, 0.5, str((i, j)),fontsize=18, ha='center')\n",
    "fig"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 泰坦尼克号数据集多子图 分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 防止中文乱码问题\n",
    "# 设置绘图的全局参数\n",
    "plt.rcParams['font.family'] = ['SimHei'] #用来显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  #用来正常显示符号"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 设置全局 字体大小和图表的大小 figsize为长16，高10英寸，字体 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 读取数据集并打印"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 绘图\n",
    "- 创建一个 1行 2 列的示例对象\n",
    "- 男性的年龄分布\n",
    "- 女性的年龄分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'female')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
